economic_value_usd float64 6 18 | human_minutes int64 6 18 | prompt stringlengths 158 504 | rate_usd_per_minute float64 1 1 | subdomain stringlengths 3 20 | task_id stringlengths 10 39 | template stringlengths 5 33 |
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7 | 7 | You are given transactions.csv (columns txn_id, date [YYYY-MM-DD], amount). Report the total amount for transactions dated between 2026-02-10 and 2026-04-20 INCLUSIVE of both endpoints, as a single JSON number rounded to 2 decimals. | 1 | retail_sales | date_windowed-1701 | date_windowed |
7 | 7 | You are given transactions.csv (columns txn_id, date [YYYY-MM-DD], amount). Report the total amount for transactions dated between 2026-01-01 and 2026-03-31 INCLUSIVE of both endpoints, as a single JSON number rounded to 2 decimals. | 1 | ecommerce | date_windowed-1702 | date_windowed |
7 | 7 | You are given transactions.csv (columns txn_id, date [YYYY-MM-DD], amount). Report the total amount for transactions dated between 2026-03-01 and 2026-03-31 INCLUSIVE of both endpoints, as a single JSON number rounded to 2 decimals. | 1 | subscriptions | date_windowed-1703 | date_windowed |
7 | 7 | You are given transactions.csv (columns txn_id, date [YYYY-MM-DD], amount). Report the total amount for transactions dated between 2026-05-01 and 2026-06-15 INCLUSIVE of both endpoints, as a single JSON number rounded to 2 decimals. | 1 | donations | date_windowed-1704 | date_windowed |
8 | 8 | You are given payments.csv (columns payment_id, customer, amount, note). Gateway retries appear as rows with the SAME payment_id and amount but a different note. Treat a payment_id as a duplicate (keep it once), then report the total amount as a single JSON number rounded to 2 decimals. | 1 | card_settlement | dedupe_then_agg-1601 | dedupe_then_agg |
8 | 8 | You are given payments.csv (columns payment_id, customer, amount, note). Gateway retries appear as rows with the SAME payment_id and amount but a different note. Treat a payment_id as a duplicate (keep it once), then report the total amount as a single JSON number rounded to 2 decimals. | 1 | subscription_billing | dedupe_then_agg-1602 | dedupe_then_agg |
8 | 8 | You are given payments.csv (columns payment_id, customer, amount, note). Gateway retries appear as rows with the SAME payment_id and amount but a different note. Treat a payment_id as a duplicate (keep it once), then report the total amount as a single JSON number rounded to 2 decimals. | 1 | marketplace_payouts | dedupe_then_agg-1603 | dedupe_then_agg |
8 | 8 | You are given payments.csv (columns payment_id, customer, amount, note). Gateway retries appear as rows with the SAME payment_id and amount but a different note. Treat a payment_id as a duplicate (keep it once), then report the total amount as a single JSON number rounded to 2 decimals. | 1 | utilities | dedupe_then_agg-1604 | dedupe_then_agg |
6 | 6 | You are given invoices.csv (columns invoice_id, amount). Report the total amount across all invoices whose amount is greater than OR EQUAL TO 250.00 (inclusive), as a single JSON number rounded to 2 decimals. | 1 | accounts_receivable | filtered_total-1401 | filtered_total |
6 | 6 | You are given invoices.csv (columns invoice_id, amount). Report the total amount across all invoices whose amount is greater than OR EQUAL TO 500.00 (inclusive), as a single JSON number rounded to 2 decimals. | 1 | procurement | filtered_total-1402 | filtered_total |
6 | 6 | You are given invoices.csv (columns invoice_id, amount). Report the total amount across all invoices whose amount is greater than OR EQUAL TO 100.00 (inclusive), as a single JSON number rounded to 2 decimals. | 1 | expenses | filtered_total-1403 | filtered_total |
6 | 6 | You are given invoices.csv (columns invoice_id, amount). Report the total amount across all invoices whose amount is greater than OR EQUAL TO 1000.00 (inclusive), as a single JSON number rounded to 2 decimals. | 1 | grants | filtered_total-1404 | filtered_total |
7 | 7 | You are given tickets.csv (columns ticket_id, team, priority). The export contains exact duplicate rows; deduplicate whole rows first, then report the number of distinct tickets per team as a JSON object mapping team to count. | 1 | it_support | grouped_count-1201 | grouped_count |
7 | 7 | You are given tickets.csv (columns ticket_id, team, priority). The export contains exact duplicate rows; deduplicate whole rows first, then report the number of distinct tickets per team as a JSON object mapping team to count. | 1 | field_service | grouped_count-1202 | grouped_count |
7 | 7 | You are given tickets.csv (columns ticket_id, team, priority). The export contains exact duplicate rows; deduplicate whole rows first, then report the number of distinct tickets per team as a JSON object mapping team to count. | 1 | helpdesk | grouped_count-1203 | grouped_count |
7 | 7 | You are given tickets.csv (columns ticket_id, team, priority). The export contains exact duplicate rows; deduplicate whole rows first, then report the number of distinct tickets per team as a JSON object mapping team to count. | 1 | facilities | grouped_count-1204 | grouped_count |
7 | 7 | You are given readings.csv (columns reading_id, site, temp_c). Some temp_c cells are blank (missing readings); EXCLUDE them from the average, do not treat them as 0. Report the mean temp_c per site as a JSON object mapping site to mean, rounded to 2 decimals. | 1 | environmental | grouped_mean-1301 | grouped_mean |
7 | 7 | You are given readings.csv (columns reading_id, site, temp_c). Some temp_c cells are blank (missing readings); EXCLUDE them from the average, do not treat them as 0. Report the mean temp_c per site as a JSON object mapping site to mean, rounded to 2 decimals. | 1 | cold_chain | grouped_mean-1302 | grouped_mean |
7 | 7 | You are given readings.csv (columns reading_id, site, temp_c). Some temp_c cells are blank (missing readings); EXCLUDE them from the average, do not treat them as 0. Report the mean temp_c per site as a JSON object mapping site to mean, rounded to 2 decimals. | 1 | greenhouse | grouped_mean-1303 | grouped_mean |
7 | 7 | You are given readings.csv (columns reading_id, site, temp_c). Some temp_c cells are blank (missing readings); EXCLUDE them from the average, do not treat them as 0. Report the mean temp_c per site as a JSON object mapping site to mean, rounded to 2 decimals. | 1 | server_room | grouped_mean-1304 | grouped_mean |
8 | 8 | You are given shipments.csv with columns shipment_id, warehouse, status, weight_kg. Some weight_kg values are negative data-entry errors: use their absolute value. Considering only rows whose status is exactly 'shipped' (case-sensitive), report the total weight_kg per warehouse as a JSON object mapping warehouse to tot... | 1 | logistics | grouped_sum-1101 | grouped_sum |
8 | 8 | You are given output.csv with columns shipment_id, warehouse, status, weight_kg. Some weight_kg values are negative data-entry errors: use their absolute value. Considering only rows whose status is exactly 'shipped' (case-sensitive), report the total weight_kg per warehouse as a JSON object mapping warehouse to total,... | 1 | steel_manufacturing | grouped_sum-1102 | grouped_sum |
8 | 8 | You are given shipments.csv with columns shipment_id, warehouse, status, weight_kg. Some weight_kg values are negative data-entry errors: use their absolute value. Considering only rows whose status is exactly 'shipped' (case-sensitive), report the total weight_kg per warehouse as a JSON object mapping warehouse to tot... | 1 | pharma_distribution | grouped_sum-1103 | grouped_sum |
8 | 8 | You are given loads.csv with columns shipment_id, warehouse, status, weight_kg. Some weight_kg values are negative data-entry errors: use their absolute value. Considering only rows whose status is exactly 'shipped' (case-sensitive), report the total weight_kg per warehouse as a JSON object mapping warehouse to total, ... | 1 | food_supply | grouped_sum-1104 | grouped_sum |
9 | 9 | You are given customers.csv (customer_id, name, tier) and orders.csv (order_id, customer_id, amount). The customers table contains a duplicate record for one customer (a CRM double-entry). Resolve the target customer to a SINGLE customer_id, then report their total order amount as a single JSON number rounded to 2 deci... | 1 | b2b_crm | join_lookup-1901 | join_lookup |
9 | 9 | You are given customers.csv (customer_id, name, tier) and orders.csv (order_id, customer_id, amount). The customers table contains a duplicate record for one customer (a CRM double-entry). Resolve the target customer to a SINGLE customer_id, then report their total order amount as a single JSON number rounded to 2 deci... | 1 | wholesale | join_lookup-1902 | join_lookup |
9 | 9 | You are given customers.csv (customer_id, name, tier) and orders.csv (order_id, customer_id, amount). The customers table contains a duplicate record for one customer (a CRM double-entry). Resolve the target customer to a SINGLE customer_id, then report their total order amount as a single JSON number rounded to 2 deci... | 1 | insurance | join_lookup-1903 | join_lookup |
9 | 9 | You are given customers.csv (customer_id, name, tier) and orders.csv (order_id, customer_id, amount). The customers table contains a duplicate record for one customer (a CRM double-entry). Resolve the target customer to a SINGLE customer_id, then report their total order amount as a single JSON number rounded to 2 deci... | 1 | telecom | join_lookup-1904 | join_lookup |
7 | 7 | You are given sales.csv (columns region, product, units). Build a pivot of total units by region x product and report the single cell for region='APAC', product='washer'. If that combination has no rows, the answer is 0 (not an error). Output a single JSON number. | 1 | retail_appliances | pivot_cell-1801 | pivot_cell |
7 | 7 | You are given sales.csv (columns region, product, units). Build a pivot of total units by region x product and report the single cell for region='LATAM', product='prem'. If that combination has no rows, the answer is 0 (not an error). Output a single JSON number. | 1 | streaming_media | pivot_cell-1802 | pivot_cell |
7 | 7 | You are given sales.csv (columns region, product, units). Build a pivot of total units by region x product and report the single cell for region='W', product='bakery'. If that combination has no rows, the answer is 0 (not an error). Output a single JSON number. | 1 | grocery | pivot_cell-1803 | pivot_cell |
7 | 7 | You are given sales.csv (columns region, product, units). Build a pivot of total units by region x product and report the single cell for region='dev', product='support'. If that combination has no rows, the answer is 0 (not an error). Output a single JSON number. | 1 | saas | pivot_cell-1804 | pivot_cell |
8 | 8 | You are given test_strings.json (a JSON list of sentences). Write a Python regex that extracts every order id of the exact form ORD- followed by EXACTLY four digits (no more, no fewer). Output only the pattern string. | 1 | order_processing | regex_extract_all-2101 | regex_extract_all |
8 | 8 | You are given test_strings.json (a JSON list of sentences). Write a Python regex that extracts every order id of the exact form ORD- followed by EXACTLY four digits (no more, no fewer). Output only the pattern string. | 1 | warehouse_logs | regex_extract_all-2102 | regex_extract_all |
8 | 8 | You are given test_strings.json (a JSON list of sentences). Write a Python regex that extracts every order id of the exact form ORD- followed by EXACTLY four digits (no more, no fewer). Output only the pattern string. | 1 | returns | regex_extract_all-2103 | regex_extract_all |
8 | 8 | You are given test_strings.json (a JSON list of sentences). Write a Python regex that extracts every order id of the exact form ORD- followed by EXACTLY four digits (no more, no fewer). Output only the pattern string. | 1 | fulfilment | regex_extract_all-2104 | regex_extract_all |
9 | 9 | You are given test_strings.json (a JSON list of log lines of the form 'DATE | user | LEVEL | message'). Write a Python regex with named groups date, user, level, message. NOTE: the message field may itself contain ' | ', so the message group must capture everything after the third delimiter. Output only the pattern str... | 1 | app_logs | regex_field_parse-2201 | regex_field_parse |
9 | 9 | You are given test_strings.json (a JSON list of log lines of the form 'DATE | user | LEVEL | message'). Write a Python regex with named groups date, user, level, message. NOTE: the message field may itself contain ' | ', so the message group must capture everything after the third delimiter. Output only the pattern str... | 1 | audit_trail | regex_field_parse-2202 | regex_field_parse |
9 | 9 | You are given test_strings.json (a JSON list of log lines of the form 'DATE | user | LEVEL | message'). Write a Python regex with named groups date, user, level, message. NOTE: the message field may itself contain ' | ', so the message group must capture everything after the third delimiter. Output only the pattern str... | 1 | access_logs | regex_field_parse-2203 | regex_field_parse |
9 | 9 | You are given test_strings.json (a JSON list of log lines of the form 'DATE | user | LEVEL | message'). Write a Python regex with named groups date, user, level, message. NOTE: the message field may itself contain ' | ', so the message group must capture everything after the third delimiter. Output only the pattern str... | 1 | syslog | regex_field_parse-2204 | regex_field_parse |
8 | 8 | You are given test_strings.json (a JSON list of objects with fields s and valid). Write a Python regex that, used with fullmatch, accepts exactly the valid product codes (two uppercase letters, dash, four digits, dash, one uppercase letter) and rejects the near-misses. Output only the pattern. | 1 | catalog | regex_validate-2301 | regex_validate |
8 | 8 | You are given test_strings.json (a JSON list of objects with fields s and valid). Write a Python regex that, used with fullmatch, accepts exactly the valid product codes (two uppercase letters, dash, four digits, dash, one uppercase letter) and rejects the near-misses. Output only the pattern. | 1 | inventory | regex_validate-2302 | regex_validate |
8 | 8 | You are given test_strings.json (a JSON list of objects with fields s and valid). Write a Python regex that, used with fullmatch, accepts exactly the valid product codes (two uppercase letters, dash, four digits, dash, one uppercase letter) and rejects the near-misses. Output only the pattern. | 1 | asset_tags | regex_validate-2303 | regex_validate |
8 | 8 | You are given test_strings.json (a JSON list of objects with fields s and valid). Write a Python regex that, used with fullmatch, accepts exactly the valid product codes (two uppercase letters, dash, four digits, dash, one uppercase letter) and rejects the near-misses. Output only the pattern. | 1 | sku_validation | regex_validate-2304 | regex_validate |
8 | 8 | The SQLite DB has an orders table (order_id, region, status, amount). Write a query returning the total amount per region for status = 'paid' ONLY (refunded rows must be excluded). Return columns (region, total). | 1 | ecommerce | sql_aggregate-2401 | sql_aggregate |
8 | 8 | The SQLite DB has an orders table (order_id, region, status, amount). Write a query returning the total amount per region for status = 'paid' ONLY (refunded rows must be excluded). Return columns (region, total). | 1 | marketplace | sql_aggregate-2402 | sql_aggregate |
8 | 8 | The SQLite DB has an orders table (order_id, region, status, amount). Write a query returning the total amount per region for status = 'paid' ONLY (refunded rows must be excluded). Return columns (region, total). | 1 | retail | sql_aggregate-2403 | sql_aggregate |
8 | 8 | The SQLite DB has an orders table (order_id, region, status, amount). Write a query returning the total amount per region for status = 'paid' ONLY (refunded rows must be excluded). Return columns (region, total). | 1 | travel | sql_aggregate-2404 | sql_aggregate |
9 | 9 | The SQLite DB has customers (customer_id, name, country) and orders (order_id, customer_id, status, amount). Write a query returning the total paid amount per customer name, for customers in country = 'UK' and orders with status = 'paid' only. | 1 | b2b | sql_join_filter-2501 | sql_join_filter |
9 | 9 | The SQLite DB has customers (customer_id, name, country) and orders (order_id, customer_id, status, amount). Write a query returning the total paid amount per customer name, for customers in country = 'US' and orders with status = 'paid' only. | 1 | saas | sql_join_filter-2502 | sql_join_filter |
9 | 9 | The SQLite DB has customers (customer_id, name, country) and orders (order_id, customer_id, status, amount). Write a query returning the total paid amount per customer name, for customers in country = 'JP' and orders with status = 'paid' only. | 1 | manufacturing | sql_join_filter-2503 | sql_join_filter |
9 | 9 | The SQLite DB has customers (customer_id, name, country) and orders (order_id, customer_id, status, amount). Write a query returning the total paid amount per customer name, for customers in country = 'NL' and orders with status = 'paid' only. | 1 | logistics | sql_join_filter-2504 | sql_join_filter |
6 | 6 | You are given sensors.csv (columns sensor_id, reading). Return the sorted JSON list of sensor_id strings whose reading is greater than OR EQUAL TO 85.0 (inclusive of the boundary). | 1 | factory_qc | threshold_flag-2001 | threshold_flag |
6 | 6 | You are given sensors.csv (columns sensor_id, reading). Return the sorted JSON list of sensor_id strings whose reading is greater than OR EQUAL TO 70.0 (inclusive of the boundary). | 1 | datacenter_thermal | threshold_flag-2002 | threshold_flag |
6 | 6 | You are given sensors.csv (columns sensor_id, reading). Return the sorted JSON list of sensor_id strings whose reading is greater than OR EQUAL TO 100.0 (inclusive of the boundary). | 1 | air_quality | threshold_flag-2003 | threshold_flag |
6 | 6 | You are given sensors.csv (columns sensor_id, reading). Return the sorted JSON list of sensor_id strings whose reading is greater than OR EQUAL TO 60.0 (inclusive of the boundary). | 1 | structural | threshold_flag-2004 | threshold_flag |
9 | 9 | You are given players.csv (columns player_id, points, joined). Return the top 5 player_ids by points, highest first. Break ties by EARLIEST joined date. Output a JSON list of player_id strings in ranked order. | 1 | gaming | top_n-1501 | top_n |
9 | 9 | You are given players.csv (columns player_id, points, joined). Return the top 3 player_ids by points, highest first. Break ties by EARLIEST joined date. Output a JSON list of player_id strings in ranked order. | 1 | sales_leaderboard | top_n-1502 | top_n |
9 | 9 | You are given players.csv (columns player_id, points, joined). Return the top 8 player_ids by points, highest first. Break ties by EARLIEST joined date. Output a JSON list of player_id strings in ranked order. | 1 | sports_league | top_n-1503 | top_n |
9 | 9 | You are given players.csv (columns player_id, points, joined). Return the top 4 player_ids by points, highest first. Break ties by EARLIEST joined date. Output a JSON list of player_id strings in ranked order. | 1 | referrals | top_n-1504 | top_n |
12 | 12 | checkins.csv (member_id, month) records each month a member checked in at a gym, covering 8 months (2026-01 through 2026-08). Return a sorted JSON list of member_id strings for every member with an unbroken run of at least 3 consecutive months checked in. | 1 | fitness_gym | consecutive_month_streak-21301 | consecutive_month_streak |
12 | 12 | checkins.csv (member_id, month) records each month a member checked in at a coworking space, covering 8 months (2026-01 through 2026-08). Return a sorted JSON list of member_id strings for every member with an unbroken run of at least 3 consecutive months checked in. | 1 | coworking_space | consecutive_month_streak-21302 | consecutive_month_streak |
12 | 12 | checkins.csv (member_id, month) records each month a patron checked in at a library, covering 8 months (2026-01 through 2026-08). Return a sorted JSON list of member_id strings for every member with an unbroken run of at least 3 consecutive months checked in. | 1 | library_visits | consecutive_month_streak-21303 | consecutive_month_streak |
12 | 12 | checkins.csv (member_id, month) records each month a member checked in at a climbing gym, covering 8 months (2026-01 through 2026-08). Return a sorted JSON list of member_id strings for every member with an unbroken run of at least 3 consecutive months checked in. | 1 | climbing_gym | consecutive_month_streak-21304 | consecutive_month_streak |
12 | 12 | campaign_events.csv (campaign_id, user_id, stage) records one row per user per funnel stage reached for each fintech campaign (stage is impression, click, or signup). Report, as a JSON object, each campaign's signup rate (distinct signup users / distinct click users), rounded to 4 decimals, for campaigns that have at l... | 1 | fintech_ads | conversion_rate_with_zero_guard-21401 | conversion_rate_with_zero_guard |
12 | 12 | campaign_events.csv (campaign_id, user_id, stage) records one row per user per funnel stage reached for each edtech campaign (stage is impression, click, or signup). Report, as a JSON object, each campaign's signup rate (distinct signup users / distinct click users), rounded to 4 decimals, for campaigns that have at le... | 1 | edtech_ads | conversion_rate_with_zero_guard-21402 | conversion_rate_with_zero_guard |
12 | 12 | campaign_events.csv (campaign_id, user_id, stage) records one row per user per funnel stage reached for each travel campaign (stage is impression, click, or signup). Report, as a JSON object, each campaign's signup rate (distinct signup users / distinct click users), rounded to 4 decimals, for campaigns that have at le... | 1 | travel_ads | conversion_rate_with_zero_guard-21403 | conversion_rate_with_zero_guard |
12 | 12 | campaign_events.csv (campaign_id, user_id, stage) records one row per user per funnel stage reached for each insurance campaign (stage is impression, click, or signup). Report, as a JSON object, each campaign's signup rate (distinct signup users / distinct click users), rounded to 4 decimals, for campaigns that have at... | 1 | insurance_ads | conversion_rate_with_zero_guard-21404 | conversion_rate_with_zero_guard |
12 | 12 | orders.csv (customer_id, order_date) records each order placed at a online boutique, order_date given as a 2026 month ('2026-01' style), covering months 2026-01 through 2026-06. Report, as a JSON object mapping month to count, the number of customers whose FIRST-EVER order falls in that month. | 1 | online_boutique | first_purchase_new_customer_count-21901 | first_purchase_new_customer_count |
12 | 12 | orders.csv (customer_id, order_date) records each order placed at a meal-kit service, order_date given as a 2026 month ('2026-01' style), covering months 2026-01 through 2026-06. Report, as a JSON object mapping month to count, the number of customers whose FIRST-EVER order falls in that month. | 1 | meal_kit_service | first_purchase_new_customer_count-21902 | first_purchase_new_customer_count |
12 | 12 | orders.csv (customer_id, order_date) records each order placed at a hardware store, order_date given as a 2026 month ('2026-01' style), covering months 2026-01 through 2026-06. Report, as a JSON object mapping month to count, the number of customers whose FIRST-EVER order falls in that month. | 1 | hardware_store | first_purchase_new_customer_count-21903 | first_purchase_new_customer_count |
12 | 12 | orders.csv (customer_id, order_date) records each order placed at a pet supply shop, order_date given as a 2026 month ('2026-01' style), covering months 2026-01 through 2026-06. Report, as a JSON object mapping month to count, the number of customers whose FIRST-EVER order falls in that month. | 1 | pet_supply_shop | first_purchase_new_customer_count-21904 | first_purchase_new_customer_count |
16 | 16 | funnel_events.csv (user_id, step, event_date) records, for each user of a mobile banking app, one row per onboarding step they completed (steps, in order: signup, verified_email, completed_profile, first_action); a user only reaches a step if they completed every step before it. Write a SQL query returning (pair, dropo... | 1 | mobile_banking_app | funnel_dropoff-22801 | funnel_dropoff |
16 | 16 | funnel_events.csv (user_id, step, event_date) records, for each user of a B2B software trial, one row per onboarding step they completed (steps, in order: trial_start, invited_teammate, connected_data, first_report); a user only reaches a step if they completed every step before it. Write a SQL query returning (pair, d... | 1 | b2b_trial_signup | funnel_dropoff-22802 | funnel_dropoff |
16 | 16 | funnel_events.csv (user_id, step, event_date) records, for each user of a dating app, one row per onboarding step they completed (steps, in order: signup, photo_uploaded, first_match); a user only reaches a step if they completed every step before it. Write a SQL query returning (pair, dropoff) rows, one per adjacent s... | 1 | dating_app | funnel_dropoff-22803 | funnel_dropoff |
16 | 16 | funnel_events.csv (user_id, step, event_date) records, for each user of a food delivery app, one row per onboarding step they completed (steps, in order: signup, address_added, payment_added, first_order); a user only reaches a step if they completed every step before it. Write a SQL query returning (pair, dropoff) row... | 1 | food_delivery_app | funnel_dropoff-22804 | funnel_dropoff |
16 | 16 | invoices.csv (vendor_id, invoice_number) records invoice numbers issued per vendor at a accounts-payable office; some numbers were voided and never stored. Write a SQL query returning (vendor_id, missing_number) for every invoice number between each vendor's lowest and highest recorded number that has no matching row. | 1 | accounts_payable | gaps_in_sequence-22001 | gaps_in_sequence |
16 | 16 | invoices.csv (vendor_id, invoice_number) records invoice numbers issued per vendor at a construction firm; some numbers were voided and never stored. Write a SQL query returning (vendor_id, missing_number) for every invoice number between each vendor's lowest and highest recorded number that has no matching row. | 1 | construction_firm | gaps_in_sequence-22002 | gaps_in_sequence |
16 | 16 | invoices.csv (vendor_id, invoice_number) records invoice numbers issued per vendor at a catering company; some numbers were voided and never stored. Write a SQL query returning (vendor_id, missing_number) for every invoice number between each vendor's lowest and highest recorded number that has no matching row. | 1 | catering_company | gaps_in_sequence-22003 | gaps_in_sequence |
16 | 16 | invoices.csv (vendor_id, invoice_number) records invoice numbers issued per vendor at a print shop; some numbers were voided and never stored. Write a SQL query returning (vendor_id, missing_number) for every invoice number between each vendor's lowest and highest recorded number that has no matching row. | 1 | print_shop | gaps_in_sequence-22004 | gaps_in_sequence |
14 | 14 | employees.csv (emp_id, department, salary) lists a tech startup's salaries. Write a SQL query returning (department, median), the median salary in each department (the middle value, or the average of the two middle values when a department has an even headcount), rounded to 2 decimals. | 1 | tech_startup | median_via_manual_computation-21801 | median_via_manual_computation |
14 | 14 | employees.csv (emp_id, department, salary) lists a hospital system's salaries. Write a SQL query returning (department, median), the median salary in each department (the middle value, or the average of the two middle values when a department has an even headcount), rounded to 2 decimals. | 1 | hospital_system | median_via_manual_computation-21802 | median_via_manual_computation |
14 | 14 | employees.csv (emp_id, department, salary) lists a law firm's salaries. Write a SQL query returning (department, median), the median salary in each department (the middle value, or the average of the two middle values when a department has an even headcount), rounded to 2 decimals. | 1 | law_firm | median_via_manual_computation-21803 | median_via_manual_computation |
14 | 14 | employees.csv (emp_id, department, salary) lists a university department's salaries. Write a SQL query returning (department, median), the median salary in each department (the middle value, or the average of the two middle values when a department has an even headcount), rounded to 2 decimals. | 1 | university_dept | median_via_manual_computation-21804 | median_via_manual_computation |
14 | 14 | mrr_snapshots.csv (product_line, month, mrr) records monthly recurring revenue snapshots per product line. Write a SQL query returning (product_line, month, pct_change), the percent change in mrr for each product line relative to that same product line's immediately preceding recorded month. | 1 | saas_billing | month_over_month_change-21201 | month_over_month_change |
14 | 14 | mrr_snapshots.csv (product_line, month, mrr) records monthly recurring revenue snapshots per product line. Write a SQL query returning (product_line, month, pct_change), the percent change in mrr for each product line relative to that same product line's immediately preceding recorded month. | 1 | media_streaming | month_over_month_change-21202 | month_over_month_change |
14 | 14 | mrr_snapshots.csv (product_line, month, mrr) records monthly recurring revenue snapshots per product line. Write a SQL query returning (product_line, month, pct_change), the percent change in mrr for each product line relative to that same product line's immediately preceding recorded month. | 1 | fitness_app | month_over_month_change-21203 | month_over_month_change |
14 | 14 | mrr_snapshots.csv (product_line, month, mrr) records monthly recurring revenue snapshots per product line. Write a SQL query returning (product_line, month, pct_change), the percent change in mrr for each product line relative to that same product line's immediately preceding recorded month. | 1 | cloud_storage | month_over_month_change-21204 | month_over_month_change |
14 | 14 | orders.csv (customer_id, order_date, amount) records subscription box service orders. For 2026-04, report a JSON object with keys 'new' and 'returning': the total revenue that month from customers whose global first-ever order falls in 2026-04 ('new'), versus customers who had already ordered at least once before 2026-... | 1 | subscription_box | new_vs_returning_revenue_split-22501 | new_vs_returning_revenue_split |
14 | 14 | orders.csv (customer_id, order_date, amount) records furniture store orders. For 2026-04, report a JSON object with keys 'new' and 'returning': the total revenue that month from customers whose global first-ever order falls in 2026-04 ('new'), versus customers who had already ordered at least once before 2026-04 ('retu... | 1 | furniture_store | new_vs_returning_revenue_split-22502 | new_vs_returning_revenue_split |
14 | 14 | orders.csv (customer_id, order_date, amount) records coffee roaster orders. For 2026-04, report a JSON object with keys 'new' and 'returning': the total revenue that month from customers whose global first-ever order falls in 2026-04 ('new'), versus customers who had already ordered at least once before 2026-04 ('retur... | 1 | coffee_roaster | new_vs_returning_revenue_split-22503 | new_vs_returning_revenue_split |
14 | 14 | orders.csv (customer_id, order_date, amount) records supplement brand orders. For 2026-04, report a JSON object with keys 'new' and 'returning': the total revenue that month from customers whose global first-ever order falls in 2026-04 ('new'), versus customers who had already ordered at least once before 2026-04 ('ret... | 1 | supplement_brand | new_vs_returning_revenue_split-22504 | new_vs_returning_revenue_split |
12 | 12 | listings.csv (listing_id, city, price) lists properties for sale. Write a SQL query returning the second-highest DISTINCT price in each city, as (city, price) rows. | 1 | real_estate | nth_highest_per_group-21001 | nth_highest_per_group |
12 | 12 | listings.csv (listing_id, city, price) lists vehicles for sale. Write a SQL query returning the second-highest DISTINCT price in each city, as (city, price) rows. | 1 | used_cars | nth_highest_per_group-21002 | nth_highest_per_group |
12 | 12 | listings.csv (listing_id, city, price) lists lots for sale. Write a SQL query returning the second-highest DISTINCT price in each city, as (city, price) rows. | 1 | art_auctions | nth_highest_per_group-21003 | nth_highest_per_group |
12 | 12 | listings.csv (listing_id, city, price) lists boats for sale. Write a SQL query returning the second-highest DISTINCT price in each city, as (city, price) rows. | 1 | boat_brokerage | nth_highest_per_group-21004 | nth_highest_per_group |
10 | 10 | orders.csv (customer_id, product_id) records grocery delivery service purchases. Return a single JSON number: the count of distinct customers who purchased BOTH 'A100' and 'B200'. | 1 | grocery_delivery | overlap_between_two_sets-22601 | overlap_between_two_sets |
10 | 10 | orders.csv (customer_id, product_id) records electronics retailer purchases. Return a single JSON number: the count of distinct customers who purchased BOTH 'LAPTOP-X' and 'DOCK-Y'. | 1 | electronics_retail | overlap_between_two_sets-22602 | overlap_between_two_sets |
10 | 10 | orders.csv (customer_id, product_id) records pet store purchases. Return a single JSON number: the count of distinct customers who purchased BOTH 'FOOD-A' and 'TOY-B'. | 1 | pet_store | overlap_between_two_sets-22603 | overlap_between_two_sets |
10 | 10 | orders.csv (customer_id, product_id) records bookstore purchases. Return a single JSON number: the count of distinct customers who purchased BOTH 'BOOK-1' and 'BOOK-2'. | 1 | bookstore | overlap_between_two_sets-22604 | overlap_between_two_sets |
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